Evasion awareness is defined as the ability of a detection model to foresee the manipulation of the input samples to intentionally fool it by an adversary. The inherent problem of decision bias in ML classifiers can lead to evasion attacks, especially in low data regimes. An adversarial evasion j* is a crafted version of an input sample j given by Equation REF , where \(\eta \)  is a perturbation, and \(\epsilon \)  is the accepted range. When making an adversarial attack, \(\eta \)  is sought and selected so that the classifier can not differentiate between j* and j [1]}, [2]}, [3]}.
\( \begin{aligned}j^{*} = |j + \eta | \le \epsilon \end{aligned}\) 
